Overview of high throughput sequencing technologies to elucidate molecular pathways in cardiovascular diseases

Molecular methods in cancer diagnostics: a short review

BACKGROUND

One of the ailments with the greatest fatality rates in the 21st century is cancer. Globally, molecular methods are widely employed to treat cancer-related disorders, and the body of research on this subject is growing yearly. A thorough and critical summary of the data supporting molecular methods for illnesses linked to cancer is required.

OBJECTIVE

In order to guide clinical practice and future research, it is important to examine and summarize the systematic reviews (SRs) that evaluate the efficacy and safety of molecular methods for disorders associated to cancer.

METHODS

We developed a comprehensive search strategy to find relevant articles from electronic databases like PubMed, Google Scholar, Web of Science (WoS), or Scopus. We looked through the literature and determined which diagnostic methods in cancer genetics were particularly reliable. We used phrases like 'cancer genetics', genetic susceptibility, Hereditary cancer, cancer risk assessment, 'cancer diagnostic tools', cancer screening', biomarkers, and molecular diagnostics, reviews and meta-analyses evaluating the efficacy and safety of molecular therapies for cancer-related disorders. Research that only consider treatment modalities that don't necessitate genetic or molecular diagnostics fall under the exclusion criteria.

RESULTS

The results of this comprehensive review clearly demonstrate the transformative impact of molecular methods in the realm of cancer genetics.This review underscores how these technologies have empowered researchers and clinicians to identify and understand key genetic alterations that drive malignancy, ranging from point mutations to structural variations. Such insights are instrumental in pinpointing critical oncogenic drivers and potential therapeutic targets, thus opening the door for methods in precision medicine that can significantly improve patient outcomes.

LIMITATION

The search does not specify a timeframe for publication inclusion, it may have missed recent advancements or changes in the field's landscape of molecular methods for cancer. As a result, it may not have included the most recent developments in the field.

CONCLUSION

After conducting an in-depth study on the molecular methods in cancer genetics, it is evident that these cutting-edge technologies have revolutionized the field of oncology, providing researchers and clinicians with powerful tools to unravel the complexities of cancer at the genetic level. The integration of molecular methods techniques has not only enhanced our understanding of cancer etiology, progression, and treatment response but has also opened new avenues for personalized medicine and targeted therapies, leading to improved patient outcomes.

Integrating multi-wet laboratory diagnostics to study staphylococci in animals in Uganda

BACKGROUND

Several diagnostic environments in Uganda lack real-time, robust and high-throughput technologies for comprehensive typing of microbes, which is a setback to infectious disease surveillance. This study combined various wet laboratory diagnostics to understand the epidemiology of pathogenic staphylococci isolated from animals in Uganda and the implications for global health security priorities.

METHODS

A retrospective study was conducted employing records and pathogenic staphylococci (from animals) archived at the Central Diagnostic Laboratory (CDL), Makerere University, Uganda, between January 2012 and December 2019. The bacteria were speciated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and tested for virulence factors [beta lactamases, lecithinase, deoxyribonuclease (DNase), haemolysins] and resistance to ten antimicrobials of clinical and veterinary relevance. Tetracycline and methicillin resistance genes were also tested.

RESULTS

The prevalent diseases were mastitis in cattle and skin infections in dogs. Of the 111 staphylococci tested by MALDI-TOF MS, 79 (71.2%) were Staphylococcus aureus, 27 (24.3%) were Staphylococcus pseudintermedius and 5 (4.5%) were Staphylococcus schleiferi. All these strains expressed haemolysins. The prevalence of strains with lecithinase, penicillinase, cephalosporinase and DNase was 35.9% (14/39), 89.7% (35/39), 0.0% (0/39) and 87.2% (34/39), respectively. Staphylococci were primarily resistant to early penicillins (over 80%), tetracycline (57.7%), and chloramphenicol (46.2%). Minimal resistance was noted with cloxacillin (0.0%), ciprofloxacin (9.6%), and cefoxitin (3.8%). The prevalence of multidrug resistance (MDR) was 78.8% for general staphylococci, 82.2% for S. aureus, 73.1% for S. pseudintermedius, and 60.0% for S. schleiferi. Multidrug resistant staphylococci were significantly more prevalent in the cattle isolates than in the dog isolates (P < 0.05). The prevalence of methicillin-resistant staphylococci (MRS) tested by resistance to cefoxitin and mecA carriage was 3.8%. These four strains were all isolated from dog skin infections. The tetK gene was the most predominant (35.4%), followed by tetM (25.0%).

CONCLUSION

In resource-constrained settings, the approach of integrated diagnostics promises sustainable disease surveillance and the addressing of current capacity gaps. The emergence of MRS (zoonotic bacteria) in companion animals creates a likelihood of reduced treatment options for related human infections, a threat to global health.

Exploration of the Key Genes Involved in Non-alcoholic Fatty Liver Disease and Possible MicroRNA Therapeutic Targets

Background

MicroRNAs (miRNAs) are promising therapeutic agents for non-alcoholic fatty liver disease (NAFLD). This study aimed to identify key genes/proteins involved in NAFLD pathogenesis and progression and to evaluate miRNAs influencing their expression.

Methods

Gene expression profiles from datasets GSE151158, GSE163211, GSE135251, GSE167523, GSE46300, and online databases were analyzed to identify significant NAFLD-related genes. Then, protein-protein interaction networks and module analysis identified hub genes/proteins, which were validated using real-time PCR in oleic acid-treated HepG2 cells. Functional enrichment analysis evaluated signaling pathways and biological processes. Gene-miRNA interaction networks identified miRNAs targeting critical NAFLD genes.

Results

The most critical overexpressed hub genes/proteins included: TNF, VEGFA, TLR4, CYP2E1, ACE, SCD, FASN, SREBF2, and TGFB1 based on PPI network analysis, of which TNF, TLR4, SCD, FASN, SREBF2, and TGFB1 were up-regulated in oleic acid-treated HepG2 cells. Functional enrichment analysis for biological processes highlighted programmed necrotic cell death, lipid metabolic process response to reactive oxygen species, and inflammation. In the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the highest adjusted P-value signaling pathways encompassed AGE-RAGE in diabetic complications, TNF, and HIF-1 signaling pathways. In gene-miRNA network analysis, miR-16 and miR-124 were highlighted as the miRNAs exerting the most influence on important NAFLD-related genes.

Conclusion

In silico analyses identified NAFLD therapeutic targets and miRNA candidates to guide further experimental investigation.

Multiple graphical views for automatically generating SQL for the MycoDiversity DB; making fungal biodiversity studies accessible

Fungi is a highly diverse group of eukaryotic organisms that live under an extremely wide range of environmental conditions. Nowadays, there is a fundamental focus on observing how biodiversity varies on different spatial scales, in addition to understanding the environmental factors which drive fungal biodiversity. Metabarcoding is a high-throughput DNA sequencing technology that has positively contributed to observing fungal communities in environments. While the DNA sequencing data generated from metabarcoding studies are available in public archives, this valuable data resource is not directly usable for fungal biodiversity investigation. Additionally, due to its fragmented storage and distributed nature, it is not immediately accessible through a single user interface. We developed the MycoDiversity DataBase User Interface (https://mycodiversity.liacs.nl) to provide direct access and retrieval of fungal data that was previously inaccessible in the public domain. The user interface provides multiple graphical views of the data components used to reveal fungal biodiversity. These components include reliable geo-location terms, the reference taxonomic scientific names associated with fungal species and the standard features describing the environment where they occur. Direct observation of the public DNA sequencing data in association with fungi is accessible through SQL search queries created by interactively manipulating topological maps and dynamic hierarchical tree views. The search results are presented in configurable data table views that can be downloaded for further use. With the MycoDiversity DataBase User Interface, we make fungal biodiversity data accessible, assisting researchers and other stakeholders in using metabarcoding studies for assessing fungal biodiversity.

Shexiang tongxin dropping pill alleviates myocardial injury induced by coronary microembolization by down-regulating APOC1 to inhibit STAT3 signaling pathway

AIM

This study determines to validate the mechanism of Shexiang Tongxin dropping pill (STDP) in attenuating coronary microembolization (CME) induced myocardial injury.

METHODS

CME rat models were established and underwent corresponding treating. Gene chip analysis was performed in rat myocardial tissues for GO and KEGG enrichment analysis. The differentially expressed genes were detected by qRT-PCR. H&E staining and ELISA were used for pathological analysis and detection of troponin (cTnI) and Creatine Kinase Isoenzyme (CK-MB). Lipopolysaccharide (LPS) treated primary cardiomyocytes were used to mimic inflammatory in vitro models. Cell viability and apoptosis of cardiomyocytes were determined by MTT and flow cytometry. The expressions of inflammatory cytokines, apoptotic proteins and proteins related to the STAT3 signal pathway were detected by western blot. APOC1 mRNA expression was detected by qRT-PCR. Immunofluorescence (IF) was used for subcellular localization of p-STAT3 and the binding of APOC1 with STAT3 was verified using Co-IP.

RESULTS

STDP can attenuate myocardial injury in CME rat models, and lead to decreased expression of APOC1 and suppressed STAT3 signal pathway. In vitro models found STDP can suppress the cell viability and cell apoptosis of primary cardiomyocytes, in addition to suppressing the secretions of IL-6, IL-1β and TNF-α, while the protective effect of STDP can be reversed by overexpression of APOC1. Co-IP found that APOC1 can bind STAT3 directly. APOC1 can increase p-STAT3 expression in the nucleus to activate the STAT3 signal pathway.

CONCLUSIONS

STDP can suppress APOC1 and STAT3 signal pathway to inhibit inflammation and cell apoptosis of cardiomyocytes. APOC1 may be one of the key regulatory factors in CME-induced myocardial injury.

Exploring ncRNA-mediated regulation of EGFR signalling in glioblastoma: From mechanisms to therapeutics

Glioblastoma (GBM) is the most prevalent and deadly primary brain tumor type, with a discouragingly low survival rate and few effective treatments. An important function of the EGFR signalling pathway in the development of GBM is to affect tumor proliferation, persistence, and treatment resistance. Advances in molecular biology in the last several years have shown how important ncRNAs are for controlling a wide range of biological activities, including cancer progression and development. NcRNAs have become important post-transcriptional regulators of gene expression, and they may affect the EGFR pathway by either directly targeting EGFR or by modifying important transcription factors and downstream signalling molecules. The EGFR pathway is aberrantly activated in response to the dysregulation of certain ncRNAs, which has been linked to GBM carcinogenesis, treatment resistance, and unfavourable patient outcomes. We review the literature on miRNAs, circRNAs and lncRNAs that are implicated in the regulation of EGFR signalling in GBM, discussing their mechanisms of action, interactions with the signalling pathway, and implications for GBM therapy. Furthermore, we explore the potential of ncRNA-based strategies to overcome resistance to EGFR-targeted therapies, including the use of ncRNA mimics or inhibitors to modulate the activity of key regulators within the pathway.

Identification and validation of regulatory T cell-associated gene signatures to predict colon adenocarcinoma prognosis

Colon adenocarcinoma (COAD) is a common cause of cancer-related death. Due to the difficulty in early diagnosis and drug resistance, conventional treatments are difficult to be effective. Some studies have found that the functional recovery of T cells in the tumor microenvironment, especially regulatory T cells (Tregs), plays an important role in the progression of cancer. This study used the TCGA data set, clinical information and RNA-seq data of COAD patients to construct a Tregs-related risk score (TRS) through methods such as WGCNA, single-factor Cox, multi-factor Cox and random survival forest (RSF). Moreover, we also used the TCGA test set and internal validation set to verify the predictive ability of TRS, and used functional enrichment analysis and somatic mutation analysis to mine genes related to TRS, such as like thrombin/trypsin receptor 2 (F2RL2), inhibin subunit beta B (INHBB) and melanoma antigen family A12 (MAGEA12). Moreover, this study confirmed the expression of these prognostic genes using scRNA-seq data. We also performed qPCR analysis of various genes in normal and cancerous colon cancer cell lines to verify that these genes indeed play a role in CODA patients. We also constructed a mouse CODA model to study and evaluate the impact of key genes such as MAGEA12 on tumor growth in mice. This study explores the important role of Treg cells in the prognosis of COAD and discovers some potential biomarkers for the occurrence and development of COAD, which provides some new ideas for the treatment of COAD.

Hypomorphic variants in inherited retinal and ocular diseases: A review of the literature with clinical cases

Hypomorphic variants decrease, but do not eliminate, gene function via a reduction in the amount of mRNA or protein product produced by a gene or by production of a gene product with reduced function. Many hypomorphic variants have been implicated in inherited retinal diseases (IRDs) and other genetic ocular conditions; however, there is heterogeneity in the use of the term "hypomorphic" in the scientific literature. We searched for all hypomorphic variants reported to cause IRDs and ocular disorders. We also discuss the presence of hypomorphic variants in the patient population of our ocular genetics department over the past decade. We propose that standardized criteria should be adopted for use of the term "hypomorphic" to describe gene variants to improve genetic counseling and patient care outcomes.

Differential gene expression and miRNA regulatory network in coronary slow flow

Coronary slow flow (CSF) is characterized by slow progression of coronary angiography without epicardial stenosis. The aim of this study was to explore the potential biomarkers and regulatory mechanism for CSF. Peripheral blood mononuclear cells from 3 cases of CSF and 3 healthy controls were collected for high-throughput sequencing of mRNA and miRNA, respectively. The differentially expressed mRNAs (DE-mRNAs) and miRNAs (DE-miRNAs) was identified. A total of 117 DE-mRNAs and 32 DE-miRNAs were obtained and they were mainly enriched in immune and inflammatory responses. Twenty-six DE-mRNAs were the predicted target genes for miRNAs by RAID, and then the regulatory network of 15 miRNAs were constructed. In addition, through the PPI network, we identified the three genes (FPR1, FPR2 and CXCR4) with larger degrees as hub genes. Among them, FPR1 was regulated by hsa-miR-342-3p, hsa-let-7c-5p and hsa-miR-197-3p and participated in the immune response. Finally, we validated the differential expression of hub genes and key miRNAs between 20 CSF and 20 control. Moreover, we found that miR-342-3p has a targeted regulatory relationship with FPR1, and their expression is negatively correlated. Then we established a hypoxia/reoxygenation (H/R) HUVEC model and detected FPR1, cell proliferation and apoptosis. Transfection with miR-342-3p mimics can significantly promote the proliferation of HUVEC under H/R conditions. FPR1 were associated with CSF as a biomarker and may be regulated by miR-342-3p potential biomarkers.

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

Exploring the biomarkers and potential therapeutic drugs for sepsis via integrated bioinformatic analysis

BACKGROUND

Sepsis is a life-threatening condition caused by an excessive inflammatory response to an infection, associated with high mortality. However, the regulatory mechanism of sepsis remains unclear.

RESULTS

In this study, bioinformatics analysis revealed the novel key biomarkers associated with sepsis and potential regulators. Three public datasets (GSE28750, GSE57065 and GSE95233) were employed to recognize the differentially expressed genes (DEGs). Taking the intersection of DEGs from these three datasets, GO and KEGG pathway enrichment analysis revealed 537 shared DEGs and their biological functions and pathways. These genes were mainly enriched in T cell activation, differentiation, lymphocyte differentiation, mononuclear cell differentiation, and regulation of T cell activation based on GO analysis. Further, pathway enrichment analysis revealed that these DEGs were significantly enriched in Th1, Th2 and Th17 cell differentiation. Additionally, five hub immune-related genes (CD3E, HLA-DRA, IL2RB, ITK and LAT) were identified from the protein-protein interaction network, and sepsis patients with higher expression of hub genes had a better prognosis. Besides, 14 drugs targeting these five hub related genes were revealed on the basis of the DrugBank database, which proved advantageous for treating immune-related diseases.

CONCLUSIONS

These results strengthen the new understanding of sepsis development and provide a fresh perspective into discriminating the candidate biomarkers for predicting sepsis as well as identifying new drugs for treating sepsis.

Microarray-based Detection of Critical Overexpressed Genes in the Progression of Hepatic Fibrosis in Non-alcoholic Fatty Liver Disease: A Protein-protein Interaction Network Analysis

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a prevalent cause of chronic liver disease and encompasses a broad spectrum of disorders, including simple steatosis, steatohepatitis, fibrosis, cirrhosis, and liver cancer. However, due to the global epidemic of NAFLD, where invasive liver biopsy is the gold standard for diagnosis, it is necessary to identify a more practical method for early NAFLD diagnosis with useful therapeutic targets; as such, molecular biomarkers could most readily serve these aims. To this end, we explored the hub genes and biological pathways in fibrosis progression in NAFLD patients.

METHODS

Raw data from microarray chips with GEO accession GSE49541 were downloaded from the Gene Expression Omnibus database, and the R package (Affy and Limma) was applied to investigate differentially expressed genes (DEGs) involved in the progress of low- (mild 0-1 fibrosis score) to high- (severe 3-4 fibrosis score) fibrosis stage NAFLD patients. Subsequently, significant DEGs with pathway enrichment were analyzed, including gene ontology (GO), KEGG and Wikipathway. In order to then explore critical genes, the protein-protein interaction network (PPI) was established and visualized using the STRING database, with further analysis undertaken using Cytoscape and Gephi software. Survival analysis was undertaken to determine the overall survival of the hub genes in the progression of NAFLD to hepatocellular carcinoma.

RESULTS

A total of 311 significant genes were identified, with an expression of 278 being upregulated and 33 downregulated in the high vs. low group. Gene functional enrichment analysis of these significant genes demonstrated major involvement in extracellular matrix (ECM)-receptor interaction, protein digestion and absorption, and the AGE-RAGE signaling pathway. The PPI network was constructed with 196 nodes and 572 edges with PPI enrichment using a p-value < 1.0 e-16. Based on this cut-off, we identified 12 genes with the highest score in four centralities: Degree, Betweenness, Closeness, and Eigenvector. Those twelve hub genes were CD34, THY1, CFTR, COL3A1, COL1A1, COL1A2, SPP1, THBS1, THBS2, LUM, VCAN, and VWF. Four of these hub genes, namely CD34, VWF, SPP1, and VCAN, showed significant association with the development of hepatocellular carcinoma.

CONCLUSION

This PPI network analysis of DEGs identified critical hub genes involved in the progression of fibrosis and the biological pathways through which they exert their effects in NAFLD patients. Those 12 genes offer an excellent opportunity for further focused research to determine potential targets for therapeutic applications.

Current Status of Omics in Biological Quality Elements for Freshwater Biomonitoring

Freshwater ecosystems have been experiencing various forms of threats, mainly since the last century. The severity of this adverse scenario presents unprecedented challenges to human health, water supply, agriculture, forestry, ecological systems, and biodiversity, among other areas. Despite the progress made in various biomonitoring techniques tailored to specific countries and biotic communities, significant constraints exist, particularly in assessing and quantifying biodiversity and its interplay with detrimental factors. Incorporating modern techniques into biomonitoring methodologies presents a challenging topic with multiple perspectives and assertions. This review aims to present a comprehensive overview of the contemporary advancements in freshwater biomonitoring, specifically by utilizing omics methodologies such as genomics, metagenomics, transcriptomics, proteomics, metabolomics, and multi-omics. The present study aims to elucidate the rationale behind the imperative need for modernization in this field. This will be achieved by presenting case studies, examining the diverse range of organisms that have been studied, and evaluating the potential benefits and drawbacks associated with the utilization of these methodologies. The utilization of advanced high-throughput bioinformatics techniques represents a sophisticated approach that necessitates a significant departure from the conventional practices of contemporary freshwater biomonitoring. The significant contributions of omics techniques in the context of biological quality elements (BQEs) and their interpretations in ecological problems are crucial for biomonitoring programs. Such contributions are primarily attributed to the previously overlooked identification of interactions between different levels of biological organization and their responses, isolated and combined, to specific critical conditions.

Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States

Phytoplankton is the essential primary producer in fresh surface water ecosystems. However, excessive phytoplankton growth due to eutrophication significantly threatens ecologic, economic, and public health. Therefore, phytoplankton identification and quantification are essential to understanding the productivity and health of freshwater ecosystems as well as the impacts of phytoplankton overgrowth (such as Cyanobacterial blooms) on public health. Microscopy is the gold standard for phytoplankton assessment but is time-consuming, has low throughput, and requires rich experience in phytoplankton morphology. Quantitative polymerase chain reaction (qPCR) is accurate and straightforward with high throughput. In addition, qPCR does not require expertise in phytoplankton morphology. Therefore, qPCR can be a useful alternative for molecular identification and enumeration of phytoplankton. Nonetheless, a comprehensive study is missing which evaluates and compares the feasibility of using qPCR and microscopy to assess phytoplankton in fresh water. This study 1) compared the performance of qPCR and microscopy in identifying and quantifying phytoplankton and 2) evaluated qPCR as a molecular tool to assess phytoplankton and indicate eutrophication. We assessed phytoplankton using both qPCR and microscopy in twelve large freshwater rivers across the United States from early summer to late fall in 2017, 2018, and 2019. qPCR- and microscope-based phytoplankton abundance had a significant positive linear correlation (adjusted R2 = 0.836, p-value < 0.001). Phytoplankton abundance had limited temporal variation within each sampling season and over the three years studied. The sampling sites in the midcontinent rivers had higher phytoplankton abundance than those in the eastern and western rivers. For instance, the concentration (geometric mean) of Bacillariophyta, Cyanobacteria, Chlorophyta, and Dinoflagellates at the sampling sites in the midcontinent rivers was approximately three times that at the sampling sites in the western rivers and approximately 18 times that at the sampling sites in the eastern rivers. Welch's analysis of variance indicates that phytoplankton abundance at the sampling sites in the midcontinent rivers was significantly higher than that at the sampling sites in the eastern rivers (p-value = 0.013) but was comparable to that at the sampling sites in the western rivers (p-value = 0.095). The higher phytoplankton abundance at the sampling sites in the midcontinent rivers was presumably because these rivers were more eutrophic. Indeed, low phytoplankton abundance occurred in oligotrophic or low trophic sites, whereas eutrophic sites had greater phytoplankton abundance. This study demonstrates that qPCR-based phytoplankton abundance can be a useful numerical indicator of the trophic conditions and water quality in freshwater rivers.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

Dual AuNPs detecting probe enhanced the NanoSPR effect for the high-throughput detection of the cancer microRNA21 biomarker

The microRNA21 (miR-21), a specific tumor biomarker, is crucial for the diagnosis of several cancer types, and investigation of its overexpression pattern is important for cancer diagnosis. Herein, we report a low-cost, rapid, ultrasensitive, and convenient biosensing strategy for the detection of miR-21 using a nanoplasmonic array chip coupled with gold nanoparticles (AuNPs). This sensing platform combines the surface plasmon resonance effect of nanoplasmonics (NanoSPR) and the localized surface plasmon resonance (LSPR) effect, which allows the real-time monitoring of the subtle optical density (OD) changes caused by the variations in the dielectric constant in the process of the hybridization of the target miRNA. Using this method, the miRNA achieves a broad detection range from 100 aM to 1 μM, and with a limit of detection (LoD) of 1.85 aM. Furthermore, this assay also has a single-base resolution to discriminate the highly homologous miRNAs. More importantly, this platform has high throughput characteristics (96 samples can be detected simultaneously). This strategy exhibits more than 86.5 times enhancement in terms of sensitivity compared to that of traditional biosensors.

Screening differential circular RNA expression profiles and the potential role of hsa_circ_0085465 in liver cancer

Aims

This study aimed to screen the circular RNAs (circRNAs) that are differentially expressed between liver cancer and paired paracarcinoma tissues and then elucidate their role in cancer progression.

Materials and Methods

High-throughput sequencing of cancer and paired paracarcinoma tissues was followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the parental genes of the differentially expressed circRNAs, which were also verified via real-time quantitative polymerase chain reaction analysis of the tissues. In addition, the function of selected circRNAs was determined using 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4- sulfophenyl)-2H-tetrazolium (MTS) and transwell assays.

Results

Total 218 and 121 circRNAs were differentially upregulated and downregulated, respectively; these were mainly enriched with GO and KEGG terms related to biological functions. From five representatives of the differentially expressed circRNAs, we selected hsa_circ_0085465 for further analysis, discovering that its overexpression promoted the proliferation, migration, and invasion of 97 L cells.

Conclusion

Taken together, our results suggest that hsa_circ_0085465 is relevant to liver cancer progression.

The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an inference framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. We leverage ML-based network inference to find networks that capture the strength of regulatory interactions. Our model first pinpoints a subset of genes, termed variational, whose expression variabilities typify the differences in network connectivity between the control and perturbed data. Variational genes, by being differentially expressed themselves or possessing differentially expressed neighbor genes, capture gene expression variability. CoVar then creates subnetworks comprising variational genes and their strongly connected neighbor genes and identifies core genes central to these subnetworks that influence the bulk of the variational activity. Through the analysis of yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar identifies key genes not found through independent differential expression analysis.

Non-coding RNA and autophagy: Finding novel ways to improve the diagnostic management of bladder cancer

Major fraction of the human genome is transcribed in to the RNA but is not translated in to any specific functional protein. These transcribed but not translated RNA molecules are called as non-coding RNA (ncRNA). There are thousands of different non-coding RNAs present inside the cells, each regulating different cellular pathway/pathways. Over the last few decades non-coding RNAs have been found to be involved in various diseases including cancer. Non-coding RNAs are reported to function both as tumor enhancer and/or tumor suppressor in almost each type of cancer. Urothelial carcinoma of the urinary bladder is the second most common urogenital malignancy in the world. Over the last few decades, non-coding RNAs were demonstrated to be linked with bladder cancer progression by modulating different signalling pathways and cellular processes such as autophagy, metastasis, drug resistance and tumor proliferation. Due to the heterogeneity of bladder cancer cells more in-depth molecular characterization is needed to identify new diagnostic and treatment options. This review emphasizes the current findings on non-coding RNAs and their relationship with various oncological processes such as autophagy, and their applicability to the pathophysiology of bladder cancer. This may offer an understanding of evolving non-coding RNA-targeted diagnostic tools and new therapeutic approaches for bladder cancer management in the future.

Nanopore Sequencing to Identify Transposable Element Insertions and Their Epigenetic Modifications

Over the past 20 years, high-throughput genomic assays have fundamentally changed how transposable elements (TEs) are studied. While short-read DNA sequencing has been at the heart of these efforts, novel technologies that generate longer reads are driving a shift in the field. Long-read sequencing now permits locus-specific approaches to locate individual TE insertions and understand their epigenetic and transcriptional regulation, while still profiling TE activity genome-wide. Here we provide detailed guidelines to implement Oxford Nanopore Technologies (ONT) sequencing to identify polymorphic TE insertions and profile TE epigenetic landscapes. Using human long interspersed element-1 (LINE-1, L1) as an example, we explain the procedures involved, including final visualization, and potential bottlenecks and pitfalls. ONT sequencing will be, in our view, a workhorse technology for the foreseeable future in the TE field.

CCK regulates osteogenic differentiation through TNFα/NF-κB in peri-implantitis

OBJECTIVE

Peri-implantitis is characterized by peri-implant mucositis and alveolar bone resorption. This study investigated cholecystokinin (CCK) expression and the mechanism underlying its involvement in peri-implantitis.

METHODS

mRNA sequencing was performed using the Gene Expression Omnibus database GSE106090. Human bone marrow mesenchymal stem cells (hBMSCs) were pretreated with various concentrations of CCK (0, 10, 30, or 100 nM) for 1 hour before induction in osteogenic differentiation medium for 2 weeks. Alkaline phosphatase (ALP) activity was determined, and the cells were stained with alizarin red. The expression levels of TNFα and the osteogenic markers ALP, RUNX2, and OCN were measured using quantitative real-time PCR. TNFα, phosphorylated P65, and total P65 levels were determined by western blot.

RESULTS

Compared with healthy individuals, 262 and 215 genes were up- and down-regulated, respectively, in the periodontal tissues of patients with peri-implantitis. CCK expression was significantly upregulated in patients with peri-implantitis. CCK reduced ALP activity, osteogenic differentiation, and levels of the osteogenic markers ALP, RUNX2, and OCN. Moreover, CCK promoted levels of TNFα and phosphorylated P65, which is a marker of activation for the NF-κB inflammatory pathway.

CONCLUSIONS

CCK regulates osteogenic differentiation through the TNFα/NF-κB axis in peri-implantitis.

Polygenic risk score and age: an extra help in the cardiovascular prevention of the young?

All major guidelines recommend assessing the risk of atherosclerotic cardiovascular disease (ASCVD) using risk scores. In fact, it has been shown that their use at the population level increases the accuracy of event prediction and facilitates the choice of strategies to be adopted in primary prevention. In fact, their use in clinical practice is far from optimal and their predictive ability on an individual level is not excellent. Our genetic heritage is substantially stable from birth and determines a ‘baseline risk’ on which external influences act. Genetic information therefore has the potential to be an early predictor of risk. Common diseases such as diabetes mellitus, ASCVD and neurodegenerative diseases are conditioned by different genetic variants with small individual effects, so that a reliable risk prediction requires careful examination of the aggregate impact of these multiple variants. The polygenic risk score (PRS) is a tool that potentially enables this complex assessment and provides a new opportunity to explore our risk of developing common diseases, including coronary artery disease (CAD). In the future, it is possible that a specific PRS could be used as an independent CAD screening tool, but this requires a detailed assessment of the practical implications, including the population to be investigated, and the consequent interventions that would then be offered.

Method for Identifying Sequence Motifs in Pre-miRNAs for Small-Molecule Binding

Non-coding RNAs are emerging targets for drug development because they are involved in various cellular processes. However, there are a few reliable design strategies for small molecules that can target RNAs. This paper reports a simple and efficient method to comprehensively analyze RNA motifs that can be bound by a specific small molecule. The method involves Dicer-mediated pre-miRNA cleavage and subsequent analysis of the reaction products by high-throughput sequencing. A pre-miRNA mutant library containing a randomized region at the Dicer cleavage site was used as the substrate for the reaction. Sequencing analysis of the products of the reaction carried out in the presence or absence of a synthetic small molecule identified the pre-miRNA mutants whose Dicer-mediated cleavage was significantly altered by the addition of the small molecule. The binding of the small molecule to the identified pre-miRNA mutants was confirmed by surface plasmon resonance, demonstrating the feasibility of our method.

Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis

BACKGROUND

Opportunistic Candida species causes severe infections when the human immune system is weakened, leading to high mortality.

METHODS

In our study, bioinformatics analysis was used to study the high-throughput sequencing data of samples infected with four kinds of Candida species. And the hub genes were obtained by statistical analysis.

RESULTS

A total of 547, 422, 415 and 405 differentially expressed genes (DEGs) of Candida albicans, Candida glabrata, Candida parapsilosis and Candida tropicalis groups were obtained, respectively. A total of 216 DEGs were obtained after taking intersections of DEGs from the four groups. A protein-protein interaction (PPI) network was established using these 216 genes. The top 10 hub genes (FOSB, EGR1, JUNB, ATF3, EGR2, NR4A1, NR4A2, DUSP1, BTG2, and EGR3) were acquired through calculation by the cytoHubba plug-in in Cytoscape software. Validated by the sequencing data of peripheral blood, JUNB, ATF3 and EGR2 genes were  significant statistical significance.

CONCLUSIONS

In conclusion, our study demonstrated the potential pathogenic genes in Candida species and their underlying mechanisms by bioinformatic analysis methods. Further, after statistical validation, JUNB, ATF3 and EGR2 genes were attained, which may be used as potential biomarkers with Candida species infection.

Involvement of m6A regulatory factor IGF2BP1 in malignant transformation of human bronchial epithelial Beas-2B cells induced by tobacco carcinogen NNK

Nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is a Group 1 human carcinogen, as classified by the International Agency for Research of Cancer (IARC), and plays a significant role in lung carcinogenesis. However, its carcinogenic mechanism has not yet been fully elucidated. In this study, we performed colony formation assays, soft-agar assays, and tumor growth in nude mice to show that 100 mg/L NNK facilitates the malignant transformation of human bronchial epithelial Beas-2B cells. Transcriptome sequencing showed that insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1), a post-transcriptional regulator, was differentially expressed in NNK-induced malignant transformed Beas-2B cells (2B-NNK cells). Small interfering RNA (SiRNA) was used to downregulate the expression of the IGF2BP1 gene. The reduction in protein expression, cell proliferation rate, and colony-forming ability and the increase in the apoptosis rate of Beas-2B cells transfected with the SiRNA indicated a role for IGF2BP1 in NNK-induced malignant transformation. IGF2BP1 is an N6-methyladenosine (m6A) regulatory factor, but it is not known whether its association with m6A mediates the malignant transformation of cells. Therefore, we measured the overall levels of m6A in Beas-2B cells. We found that the overall m6A level was lower in 2B-NNK cells, and knocking down IGF2BP1, the overall level of m6A was restored. Hence, we concluded that IGF2BP1 is involved in the NNK-induced malignant transformation of Beas-2B cells, possibly via m6A modification. This study therefore contributes novel insights into the environmental pathogenesis of lung cancer and the gene regulatory mechanisms of chemical carcinogenesis.

Phage therapeutics: from promises to practices and prospectives

The rise in multi-drug resistant bacteria and the inability to develop novel antibacterial agents limits our arsenal against infectious diseases. Antibiotic resistance is a global issue requiring an immediate solution, including the development of new antibiotic molecules and other alternative modes of therapy. This article highlights the mechanism of bacteriophage treatment that makes it a real solution for multidrug-resistant infectious diseases. Several case reports identified phage therapy as a potential solution to the emerging challenge of multi-drug resistance. Bacteriophages, unlike antibiotics, have special features, such as host specificity and do not impact other commensals. A new outlook has also arisen with recent advancements in the understanding of phage immunobiology, where phages are repurposed against both bacterial and viral infections. Thus, the potential possibility of phages in COVID-19 patients with secondary bacterial infections has been briefly elucidated. However, significant obstacles that need to be addressed are to design better clinical studies that may contribute to the widespread use of bacteriophage therapy against multi-drug resistant pathogens. In conclusion, antibacterial agents can be used with bacteriophages, i.e. bacteriophage-antibiotic combination therapy, or they can be administered alone in cases when antibiotics are ineffective.Key points• AMR, a consequence of antibiotic generated menace globally, has led to the resurgence of phage therapy as an effective and sustainable solution without any side effects and high specificity against refractory MDR bacterial infections.• Bacteriophages have fewer adverse reactions and can thus be used as monotherapy as well as in conjunction with antibiotics.• In the context of the COVID-19 pandemic, phage therapy may be a viable option.

Harnessing the Power of Metabarcoding in the Ecological Interpretation of Plant-Pollinator DNA Data: Strategies and Consequences of Filtering Approaches

Although DNA metabarcoding of pollen mixtures has been increasingly used in the field of pollination biology, methodological and interpretation issues arise due to its high sensitivity. Filtering or maintaining false positives, contaminants, and rare taxa or molecular features could lead to different ecological results. Here, we reviewed how this choice has been addressed in 43 studies featuring pollen DNA metabarcoding, which highlighted a very high heterogeneity of filtering methods. We assessed how these strategies shaped pollen assemblage composition, species richness, and interaction networks. To do so, we compared four processing methods: unfiltering, filtering with a proportional 1% of sample reads, a fixed threshold of 100 reads, and the ROC approach (Receiver Operator Characteristic). The results indicated that filtering impacted species composition and reduced species richness, with ROC emerging as a conservative approach. Moreover, in contrast to unfiltered networks, filtering decreased network Connectance and Entropy, and it increased Modularity and Connectivity, indicating that using cut-off thresholds better describes interactions. Overall, unfiltering might compromise reliable ecological interpretations, unless a study targets rare species. We discuss the suitability of each filtering type, plead for justifying filtering strategies on biological or methodological bases and for developing shared approaches to make future studies more comparable.

Identification of Abundant and Functional dodecaRNAs (doRNAs) Derived from Ribosomal RNA

Using a modified RNA-sequencing (RNA-seq) approach, we discovered a new family of unusually short RNAs mapping to ribosomal RNA 5.8S, which we named dodecaRNAs (doRNAs), according to the number of core nucleotides (12 nt) their members contain. Using a new quantitative detection method that we developed, we confirmed our RNA-seq data and determined that the minimal core doRNA sequence and its 13-nt variant C-doRNA (doRNA with a 5' Cytosine) are the two most abundant doRNAs, which, together, may outnumber microRNAs. The C-doRNA/doRNA ratio is stable within species but differed between species. doRNA and C-doRNA are mainly cytoplasmic and interact with heterogeneous nuclear ribonucleoproteins (hnRNP) A0, A1 and A2B1, but not Argonaute 2. Reporter gene activity assays suggest that C-doRNA may function as a regulator of Annexin II receptor (AXIIR) expression. doRNAs are differentially expressed in prostate cancer cells/tissues and may control cell migration. These findings suggest that unusually short RNAs may be more abundant and important than previously thought.

CAFU: a Galaxy framework for exploring unmapped RNA-Seq data

A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU.

Healthy Patients Are Not the Best Controls for Microbiome-Based Clinical Studies: Example of Sjögren's Syndrome in a Systematic Review

Introduction

It has been hypothesized that gut and oral dysbiosis may contribute to the development of primary Sjögren's syndrome (pSS). The aim of this systematic review was to assemble available data regarding the oral and gut microbiota in pSS and to compare them to data from healthy individuals and patients with dry symptoms without a diagnosis of Sjögren's syndrome or lupus disease to identify dysbiosis and discuss the results.

Methodology

Using the PRISMA guidelines, we systematically reviewed studies that compared the oral and gut microbiota of Sjögren's patients and controls. The PubMed database and Google Scholar were searched.

Results

Two-hundred and eighty-nine studies were found, and 18 studies were included: 13 referred to the oral microbiota, 4 referred to the gut microbiota, and 1 referred to both anatomical sites. The most frequent controls were healthy volunteers and patients with sicca symptoms. The most common analysis method used was 16S-targeted metagenomics. The results were mostly heterogeneous, and the results regarding diversity were not always in accordance. Dysbiosis in pSS was not confirmed, and reduced salivary secretion seems to explain more microbial changes than the underlying disease.

Conclusion

These heterogeneous results might be explained by the lack of a standardized methodology at each step of the process and highlight the need for guidelines. Our review provides evidence that sicca patients seem to be more relevant than healthy subjects as a control group.

Gastrodin Exerts Cardioprotective Action via Inhibition of Insulin-Like Growth Factor Type 2/Insulin-Like Growth Factor Type 2 Receptor Expression in Cardiac Hypertrophy

Pathological cardiac hypertrophy is commonly associated with an upregulation of fetal genes, fibrosis, cardiac dysfunction, and heart failure. Previous studies have demonstrated that gastrodin (GAS) exerts cardioprotective action in the treatment of cardiac hypertrophy. However, the mechanism by which GAS protects against cardiac hypertrophy is yet to be elucidated. A mouse model of myocardial hypertrophy was established using an angiotensin II (Ang II) induction. GAS (5 or 50 mg/kg/d) was orally administered every day starting 7 days prior to the Ang II infusion combined with sham-operated controls. Heart samples from each group were collected for RNA sequencing. Using bioinformatics analysis, the key differentially expressed genes (DEGs) that are involved in reversing cardiac function were identified. Through bioinformatics analysis, the key DEGs that are involved in GAS's inhibition of Ang II-induced abnormal gene expression within the heart were identified. This was further validated using quantitative real-time PCR and Western blotting in neonatal rat cardiomyocytes (NRCMs). Oral administration of GAS significantly suppressed the Ang II-induced increase in heart size and heart weight to body weight. Furthermore, pretreatment of the NRCMs with GAS led to a dose-dependent inhibition of Ang II-induced increases in Nppb mRNA expression. We identified 620 upregulated and 87 downregulated Ang II-induced DEGs II, among which the expression patterns of 58 and 146 genes were inverted by low-dose and high-dose GAS, respectively. These inverted DEGs were found to be mainly enriched in the biological processes of regulation of Ras protein signal transduction, heart contraction, covalent chromatin modification, glucose metabolism, and positive regulation of cell cycle. Among them, the insulin-like growth factor type 2 (Igf2) gene, which was found to be highly reversed and downregulated by GAS, served as a core gene linking energy metabolism, immune regulation, and systemic development. Subsequent functional verification demonstrated that IGF2, and its receptor IGF2R, is one of the targets of GAS that helps protect against cardiac hypertrophy. Taken together, we have identified, for the first time, IGF2/IGF2R as a potential target influenced by GAS in the prevention of cardiac hypertrophy.

Monogenic and Polygenic Models of Coronary Artery Disease

PURPOSE OF THE REVIEW

Coronary artery disease (CAD) is a common disease globally attributable to the interplay of complex genetic and lifestyle factors. Here, we review how genomic sequencing advances have broadened the fundamental understanding of the monogenic and polygenic contributions to CAD and how these insights can be utilized, in part by creating polygenic risk estimates, for improved disease risk stratification at the individual patient level.

RECENT FINDINGS

Initial studies linking premature CAD with rare familial cases of elevated blood lipids highlighted high-risk monogenic contributions, predominantly presenting as familial hypercholesterolemia (FH). More commonly CAD genetic risk is a function of multiple, higher frequency variants each imparting lower magnitude of risk, which can be combined to form polygenic risk scores (PRS) conveying significant risk to individuals at the extremes. However, gaps remain in clinical validation of PRSs, most notably in non-European populations. With improved and more broadly utilized genomic sequencing technologies, the genetic underpinnings of coronary artery disease are being unraveled. As a result, polygenic risk estimation is poised to become a widely used and powerful tool in the clinical setting. While the use of PRSs to augment current clinical risk stratification for optimization of cardiovascular disease risk by lifestyle change or therapeutic targeting is promising, we await adequately powered, prospective studies, demonstrating the clinical utility of polygenic risk estimation in practice.

Identifying genetic risk variants associated with noise-induced hearing loss based on a novel strategy for evaluating individual susceptibility

BACKGROUND

The overall genetic profile for noise-induced hearing loss (NIHL) remains elusive. Herein we proposed a novel machine learning (ML) based strategy to evaluate individual susceptibility to NIHL and identify the underlying genetic risk variants based on a subsample of participants with extreme phenotypes.

METHODS

Five features (age, sex, cumulative noise exposure [CNE], smoking, and alcohol drinking status) of 5,539 shipbuilding workers from large cross-sectional surveys were included in four ML classification models to predict their hearing levels. The area under the curve (AUC) and prediction accuracy were exploited to evaluate the performance of the models. Based on the prediction error of the ML models, the NIHL-susceptible group (n=150) and NIHL-resistant group (n=150) with a paradoxical relationship between hearing levels and features were separately screened, to identify the underlying variants associated with NIHL risk using whole-exome sequencing (WES). Subsequently, candidate risk variants were validated in an additional replication cohort (n=2108), followed by a meta-analysis.

RESULTS

With 10-fold cross-validation, the performances of the four ML models were robust and similar, with average AUCs and accuracies ranging from 0.783 to 0.798 and 73.7% to 73.8%, respectively. The phenotypes of the NIHL-susceptible and NIHL-resistant groups were significantly different (all p<0.001). After WES analysis and filtering, 12 risk variants contributing to NIHL susceptibility were identified and replicated. The meta-analyses showed that the A allele of CDH23 rs41281334 (odds ratio [OR]=1.506, 95% confidence interval [CI]=1.106-2.051) and the C allele of WHRN rs12339210 (OR=3.06, 95% CI=1.398-6.700) were significantly associated with increased risk of NIHL after adjustment for confounding factors.

CONCLUSIONS

This study revealed two genetic variants in CDH23 rs41281334 and WHRN rs12339210 that associated with NIHL risk, based on a promising approach for evaluating individual susceptibility using ML models.

Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production

The microbial diversity in anaerobic digestion (AD) is important because it affects process robustness. High-throughput sequencing offers high-resolution data regarding the microbial diversity and robustness of biological systems including AD; however, to understand the dynamics of microbial processes, knowing the microbial diversity is not adequate alone. Advanced meta-omic techniques have been established to determine the activity and interactions among organisms in biological processes like AD. Results of these methods can be used to identify biomarkers for AD states. This can aid a better understanding of system dynamics and be applied to producing comprehensive models for AD. The paper provides valuable knowledge regarding the possibility of integration of molecular methods in AD. Although meta-genomic methods are not suitable for on-line use due to long operating time and high costs, they provide extensive insight into the microbial phylogeny in AD. Meta-proteomics can also be explored in the demonstration projects for failure prediction. However, for these methods to be fully realised in AD, a biomarker database needs to be developed.

Advances in understanding the molecular pathology of gynecological malignancies: the role and potential of RNA sequencing

For many years technological limitations restricted the progress of identifying the underlying genetic causes of gynecologicalcancers. However, during the past decade, high-throughput next-generation sequencing technologies have revolutionized cancer research. RNA sequencing has arisen as a very useful technique in expanding our understanding of genome changes in cancer. Cancer is characterized by the accumulation of genetic alterations affecting genes, including substitutions, insertions, deletions, translocations, gene fusions, and alternative splicing. If these aberrant genes become transcribed, aberrations can be detected by RNA sequencing, which will also provide information on the transcript abundance revealing the expression levels of the aberrant genes. RNA sequencing is considered the technique of choice when studying gene expression and identifying new RNA species. This is due to the quantitative and qualitative improvement that it has brought to transcriptome analysis, offering a resolution that allows research into different layers of transcriptome complexity. It has also been successful in identifying biomarkers, fusion genes, tumor suppressors, and uncovering new targets responsible for drug resistance in gynecological cancers. To illustrate that we here review the role of RNA sequencing in studies that enhanced our understanding of the molecular pathology of gynecological cancers.

Methods for exploring the faecal microbiome of premature infants: a review

The premature infant gut microbiome plays an important part in infant health and development, and recognition of the implications of microbial dysbiosis in premature infants has prompted significant research into these issues. The approaches to designing investigations into microbial populations are many and varied, each with its own benefits and limitations. The technique used can influence results, contributing to heterogeneity across studies. This review aimed to describe the most common techniques used in researching the preterm infant microbiome, detailing their various limitations. The objective was to provide those entering the field with a broad understanding of available methodologies, so that the likely effects of their use can be factored into literature interpretation and future study design. We found that although many techniques are used for characterising the premature infant microbiome, 16S rRNA short amplicon sequencing is the most common. 16S rRNA short amplicon sequencing has several benefits, including high accuracy, discoverability and high throughput capacity. However, this technique has limitations. Each stage of the protocol offers opportunities for the injection of bias. Bias can contribute to variability between studies using 16S rRNA high throughout sequencing. Thus, we recommend that the interpretation of previous results and future study design be given careful consideration.

ENAM gene associated with T classification and inhibits proliferation in renal clear cell carcinoma

The potential involvement of T classification-related genes in renal clear cell carcinoma (ccRCC) must be further explored. Public data were obtained from The Cancer Genome Atlas (TCGA) database. An overall survival (OS) predictive model was developed and validated (TCGA train, 5 years, AUC = 0.73, 3 years, AUC = 0.73, 1 year, AUC = 0.76; TCGA test, 5 years, AUC = 0.74, 3 years, AUC = 0.65, 1 year, AUC = 0.73; TCGA all, 5 years, AUC = 0.72, 3 years, AUC = 0.71, 1 year, AUC = 0.75). Finally, ENAM was selected for further analysis. In vitro experiment indicated that ENMA is downregulated in ccRCC, and its knockdown could promote proliferation in two cancer cell lines (OSRC-2 and SW839). Immune infiltration analysis revealed that ENAM could remarkably increase the content of cytotoxic cells, NK CD56 cells, NK cells and CD8+ T cells in the tumor immune microenvironment, which may be one reason for its tumor-inhibiting effect. In summary, ENAM may suppress cell proliferation in ccRCC and can be used as a potential reference value for the relief and immunotherapy of ccRCC.

DNA Translocation through Vertically Stacked 2D Layers of Graphene and Hexagonal Boron Nitride Heterostructure Nanopore

Cost-effective, fast, and reliable DNA sequencing can be enabled by advances in nanopore-based methods, such as the use of atomically thin graphene membranes. However, strong interaction of DNA bases with graphene leads to undesirable effects such as sticking of DNA strands to the membrane surface. While surface functionalization is one way to counter this problem, here, we present another solution based on a heterostructure nanopore system, consisting of a monolayer of graphene and hexagonal boron nitride (hBN) each. Molecular dynamics studies of DNA translocation through this heterostructure nanopore revealed a surprising and crucial influence of the heterostructure layer order in controlling the base specific signal variability. Specifically, the heterostructure with graphene on top of hBN had nearly 3-10× lower signal variability than the one with hBN on top of graphene. Simulations point to the role of differential underside sticking of DNA bases as a possible reason for the observed influence of the layer order. Our studies can guide the development of experimental systems to study and exploit DNA translocation through two-dimensional heterostructure nanopores for single molecule sequencing and sensing applications.

Markers of Immune Function in Heart Transplantation: Implications for Immunosuppression and Screening for Rejection

PURPOSE OF REVIEW

Recent developments in high-throughput DNA and RNA sequencing technologies have facilitated the development of noninvasive assays to monitor heart transplant rejection. In this review, we summarize existing assays employed for the surveillance of allograft rejection, as well as promising future directions for such tests in the molecular biology field.

RECENT FINDINGS

The AlloMap genome expression profiling assay remains the only noninvasive test for rejection surveillance and is incorporated into the International Society of Heart and Lung Transplantation guidelines. Other efforts have focused on messenger RNA (mRNA), microRNA (miRNA), and donor-derived cell-free DNA (dd-cfDNA) as potential viable biomarkers. Mitochondrial pathways in allograft necroptosis and inflammation signaling may represent a novel direction for future research endeavors. Although endomyocardial biopsy remains the gold standard, several converging areas of molecular biology could soon yield successful alternative methods of heart transplant rejection monitoring, with the distinct advantage of avoiding procedural complications.

C-reactive protein as a possible marker for severity and mortality of COVID-19 infection

AIM

The present study aimed to introduce a possible biomarker to differentiate between severe and fatal conditions of COVID-19.

BACKGROUND

The COVID-19 pandemic, appearing as a complicated health problem, has changed the lifestyle of people in recent years. Clinical findings indicate mild, severe, and fatal conditions of this disease. Prediction of disease severity is a significant point in managing COVID-19 infection.

METHODS

In this study, 195 differentially expressed genes (DEGs) that discriminate between fatal and severe conditions in patients were extracted from the literature and screened to determine the significant ones. The significant DEGs plus the 90 first neighbors added from the STRING database were included in the interactome using Cytoscape software v 3.7.2. The central nodes of the analyzed network were identified and assessed.

RESULTS

Ten significant DEGs were candidates for assessment, of which 9 were recognized by the STRING database. IL6, ALB, TNF, CRP, INS, MPO, C3, CXCL8, TTR, and TLR4 were determined as central nodes; IL6, CRP, and TTR were highlighted as the critical genes related to the severity of COVID-19 infection.

CONCLUSION

CRP was identified as the best possible biomarker with levels related to the severity and fatality of COVID-19 infection.

Development of Diagnostic Tests for Detection of SARS-CoV-2

One of the most effective ways to prevent the spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is to develop accurate and rapid diagnostic tests. There are a number of molecular, serological, and imaging methods that are used to diagnose this infection in hospitals and clinical settings. The purpose of this review paper is to present the available approaches for detecting SARS-CoV-2 and address the advantages and limitations of each detection method. This work includes studies from recent literature publications along with information from the manufacturer's manuals of commercially available SARS-CoV-2 diagnostic products. Furthermore, supplementary information from the Food & Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) is cited. The viral components targeted for virus detection, the principles of each diagnostic technique, and the detection efficiency of each approach are discussed. The potential of using diagnostic tests that were originally developed for previous epidemic viruses is also presented.

Taking the next steps to implement polygenic risk scoring for improved risk stratification and primary prevention of coronary artery disease

Coronary artery disease (CAD) remains the leading cause of death worldwide. The role of hypertension, cholesterol, diabetes mellitus, and smoking in driving disease has been well recognized at a population level and has been the target of primary prevention strategies for over 50 years with substantial impact. However, in many cases, these factors alone do not provide enough precision at the individual level to allow physicians and patients to take appropriate preventive measures and many patients continue to suffer acute coronary syndromes in the absence of these risk factors. Recent advances in user-friendly chip designs, high speed throughput, and economic efficiency of genome-wide association studies complemented by advances in statistical analytical approaches have facilitated the rapid development of polygenic risk scores (PRSs). The latest PRSs combine data regarding hundreds of thousands of single-nucleotide polymorphisms to predict chronic diseases including CAD. Novel CAD PRSs are strong predictors of risk and may have application, in a complementary manner with existing risk prediction algorithms. However, there remain substantial controversies, and ultimately, we need to move forward from observational studies to prospectively and rigorously assess the potential impact if widespread implementation is to be aspired to. Consideration needs to be made of ethnicity, sex, as well as age, and risk estimate based on existing non-genomic algorithms. We provide an overview and commentary on the important advances in deriving and validating PRSs, as well as pragmatic considerations that will be required for implementation of the new knowledge into clinical practice.

High-Throughput Sequencing is a Crucial Tool to Investigate the Contribution of Human Endogenous Retroviruses (HERVs) to Human Biology and Development

Human Endogenous retroviruses (HERVs) are remnants of ancient retroviral infections that represent a large fraction of our genome. Their transcriptional activity is finely regulated in early developmental stages and their expression is modulated in different cell types and tissues. Such activity has an impact on human physiology and pathology that is only partially understood up to date. Novel high-throughput sequencing tools have recently allowed for a great advancement in elucidating the various HERV expression patterns in different tissues as well as the mechanisms controlling their transcription, and overall, have helped in gaining better insights in an all-inclusive understanding of the impact of HERVs in biology of the host.

An Assessment of the Bacterial Diversity found in Dental Unit Waterlines using the Illumina MiSeq

Water from the waterlines of dental units is often contaminated with bacteria but there have been few studies accurately assessing the diversity of these bacterial populations. The aim of our study was to assess the bacterial diversity present in water collected from dental unit waterlines using the Illumina MiSeq. Water was collected from two separate dental units located in a dental hospital and two units found in two separate private clinics in Gangneung-si, Korea. From the four water samples that were analyzed, a total of 233 bacterial genera were identified. The most abundant genera were Sphingomonas (25%), Halomonas (20%), Reyranella (8%), and Novosphingobium (6%). Halomonas was more prevalent in the two dental units located at the dental hospital, while Reyranella and Sphingomonas were more commonly found in the private dental clinics. Only 19 of the 233 identified genera were common between water samples from all dental units. Opportunistic pathogens were shown to account for 7.7% of the total bacterial genera identified. Our results have demonstrated that there is a wide assortment of bacterial genera present in dental unit waterlines.

Mapping and editing of nucleic acid modifications

Modification on nucleic acid plays a pivotal role in controlling gene expression. Various kinds of modifications greatly increase the information-encoding capacity of DNA and RNA by introducing extra chemical group to existing bases instead of altering the genetic sequences. As a marker on DNA or RNA, nucleic acid modification can be recognized by specific proteins, leading to versatile regulation of gene expression. However, modified and regular bases are often indistinguishable by most conventional molecular methods, impeding detailed functional studies that require the information of genomic location. Recently, new technologies are emerging to resolve the positions of varied modifications on both DNA and RNA. Intriguingly, by integrating regional targeting tools and effector proteins, researchers begin to actively control the modification status of desired gene in vivo. In this review, we summarize the characteristics of DNA and RNA modifications, the available mapping and editing tools, and the potential application as well as deficiency of these technologies in basic and translational researches.

Identification of potential functional variants underlying ovine resistance to gastrointestinal nematode infection by using RNA-Seq

In dairy sheep flocks from Mediterranean countries, replacement and adult ewes are the animals most affected by gastrointestinal nematode (GIN) infections. In this study, we have exploited the information derived from an RNA-Seq experiment with the aim of identifying potential causal mutations related to GIN resistance in sheep. Considering the RNA-Seq samples from 12 ewes previously classified as six resistant and six susceptible animals to experimental infection by Teladorsagia circumcincta, we performed a variant calling analysis pipeline using two different types of software, gatk version 3.7 and Samtools version 1.4. The variants commonly identified by the two packages (high-quality variants) within two types of target regions - (i) QTL regions previously reported in sheep for parasite resistance based on SNP-chip or sequencing technology studies and (ii) functional candidate genes selected from gene expression studies related to GIN resistance in sheep - were further characterised to identify mutations with a potential functional impact. Among the genes harbouring these potential functional variants (930 and 553 respectively for the two types of regions), we identified 111 immune-related genes in the QTL regions and 132 immune-related genes from the initially selected candidate genes. For these immune-related genes harbouring potential functional variants, the enrichment analyses performed highlighted significant GO terms related to apoptosis, adhesion and inflammatory response, in relation to the QTL related variants, and significant disease-related terms such as inflammation, adhesion and necrosis, in relation to the initial candidate gene list. Overall, the study provides a valuable list of potential causal mutations that could be considered as candidate causal mutations in relation to GIN resistance in sheep. Future studies should assess the role of these suggested mutations with the aim of identifying genetic markers that could be directly implemented in sheep breeding programmes considering not only production traits, but also functional traits such as resistance to GIN infections.

An updated review on phenocopies of primary immunodeficiency diseases

Primary immunodeficiency diseases (PIDs) refer to a heterogenous group of disorders characterized clinically by increased susceptibility to infections, autoimmunity and increased risk of malignancies.

These group of disorders present with clinical manifestations similar to PIDs with known genetic defects but have either no genetic defect or have a somatic mutation and thus have been labelled as “Phenocopies of PIDs”. These diseases have been further subdivided into those associated with somatic mutations and those associated with presence of auto-antibodies against various cytokines.

In this review, we provide an update on clinical manifestations, diagnosis and management of these diseases.